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Learn About Interdisciplinary Research

Research approaches.

• Interdisciplinary Research
• Convergence Research
• Transformative Research

The U.S. National Science Foundation gives high priority to research that is interdisciplinary — transcending the scope of a single discipline or program.

NSF's support of interdisciplinary research and education is essential for accelerating scientific discovery and preparing a workforce that addresses scientific challenges in innovative ways.

This page covers the ways NSF supports interdisciplinary research and how to prepare an interdisciplinary proposal, including how to submit an unsolicited proposal when there is no natural " home " for it in one of NSF’s existing programs.

On this page

• ● What is interdisciplinary research?
• ● How does NSF support interdisciplinary research?
• ● How to prepare an interdisciplinary proposal
• ● Who to contact
• ● Frequently asked questions (FAQ)

What is interdisciplinary research?

Credit: Jurgen Schulze, University of California, San Diego

The definition of a "discipline" and the varieties of cross-disciplinary research — from multidisciplinary, to interdisciplinary, to transdisciplinary — are constantly evolving. Although there is not always agreement on these definitions, it is clear that areas of research are dynamic: continually emerging, melding and transforming. What is considered interdisciplinary today might be considered disciplinary tomorrow.

A working definition of interdisciplinary research can be found in the U.S. National Academies of Sciences, Engineering and Medicine's report, Facilitating Interdisciplinary Research :

Interdisciplinary research:

• Integrates information, data, techniques, tools, perspectives, concepts or theories from two or more disciplines or bodies of specialized knowledge.
• Can be done by teams or by individuals.
• Advances fundamental understanding or solves problems whose solutions are beyond the scope of a single discipline or area of research practice.

How does NSF support interdisciplinary research?

1. Solicited interdisciplinary research

Numerous NSF programs are designed to be explicitly interdisciplinary. Solicitations, which invite proposals to these programs, are posted on the NSF website . NSF's interdisciplinary research programs broadly fall under the three categories below:

Cross-cutting programs

Many of NSF's interdisciplinary programs involve several of NSF's directorates. Examples of these programs include:

• Building Theoretical Foundations for Data Sciences (TRIPODS)
• Coastlines and People
• Dynamics of Integrated Socio-Environmental Systems
• Ecology and Evolution of Infectious Diseases  
• Growing Convergence Research
• Research on Emerging Technologies for Teaching and Learning
• Smart and Connected Communities

Areas of national importance

NSF develops funding portfolios that focus on complex societal challenges of national interest, often in collaboration with other federal agencies. Examples of these programs include:

• The Future of Work at the Human-Technology Frontier
• National Artificial Intelligence Research Institutes
• Navigating the New Arctic
• Understanding the Rules of Life

Center competitions

Many of the centers funded by NSF bring together interdisciplinary research teams. Examples of NSF's center competitions include:

• Materials Research Science and Engineering Centers
• Science and Technology Centers

2. Unsolicited interdisciplinary research

NSF invites interdisciplinary proposals that are not targeted by a program solicitation, as long as they are appropriate for NSF support . Depending on its focus, such a proposal may:

• Be reviewed by a single core program.
• Be co-reviewed by more than one program.
• Extend beyond the scope of any current program.

See " How to prepare an interdisciplinary proposal " to learn how to submit an unsolicited interdisciplinary research proposal.

3. Education and training

NSF has numerous programs supporting the development of the next generation of researchers. The support from these programs is in addition to the support for undergraduates, graduate students and postdoctoral researchers to conduct research on NSF-funded grants. Examples of these programs include:

• Research Traineeship Program
• Research Experiences for Undergraduates

4. Workshops, conferences and symposiums

NSF sponsors forums designed to promote interdisciplinary perspectives and research.

How to prepare an interdisciplinary proposal

Preparing an unsolicited interdisciplinary proposal.

Follow the guidance below for how to submit a proposal with ideas that are in novel or emerging areas extending beyond any particular NSF program.

1. Prepare a summary of your proposal ideas.

Develop a short 1–2 paragraph description of your proposal idea that you can send by email and discuss with NSF staff. Make sure your idea is appropriate for NSF funding by viewing the Programs and Funding Opportunities section of the agency's Proposal and Award Policies and Procedures Guide .

2. Contact an NSF program officer.

The program officer you contact will provide guidance on how and where to submit your proposal. To find an appropriate program officer, consider these options in the following order:

• Identify a program officer through an existing NSF program. In many cases, there will be an existing NSF program for which the proposal idea may be appropriate. Read the program description or solicitation. If your idea seems appropriate, contact one of the program’s program officers.
• Identify a program officer through other means. If your proposal doesn’t clearly fit an existing program, it may make sense to first contact a program officer with expertise in your discipline. They may consult with other NSF staff or recommend another officer for you to contact. You may also contact a program officer you already know, such as one who is managing an award for you or who you met at a conference. 
• Contact a point of contact listed below. If you think your proposal will be of particular interest to one NSF directorate or office, reach out to the relevant point of contact for that directorate. That person is responsible for identifying a program officer in that directorate who will discuss your proposal with you.

Points of contact:

The contacts below are responsible for identifying a program officer in their directorate who will discuss your proposal with you.

If there is not an obvious point of contact from one of the options below, email NSF at [email protected] or call (703) 292-4840.

Cross-directorate, NSF-wide

Jessica Robin, OD/OISE

Telephone: (703) 292-8706

Email: [email protected]

Office of Integrative Activities

Randy Phelps, OD/OIA

Telephone: (703) 292-5049

Email: [email protected]

Directorate for Biological Sciences

James O. Deshler, BIO/DBI

Telephone: (703) 292-7871

Email: [email protected]

Directorate for Computer and Information Science and Engineering

James Donlon, CISE/CCF

Telephone: (703) 292-8074

Email: [email protected]

Directorate for Education and Human Resources

Gregg E. Solomon, EHR/DRL

Telephone: (703) 292-8333

Email: [email protected]

Directorate for Engineering

Sohi Rastegar, ENG/OAD

Telephone: (703) 292-5379

Email: [email protected]

Directorate for Geosciences

Barbara Ransom , GEO/OAD

Telephone: (703) 292-7792

Email: [email protected]

Directorate for Mathematical and Physical Sciences

Dean Evasius, MPS/OAD

Telephone: (703) 292-7352

Email: [email protected]

Directorate for Social, Behavioral and Economic Sciences

Brian Humes, SBE/SES

Telephone: (703) 292-7281

Email: [email protected]

Preparing a proposal for an existing program?

If you are submitting a proposal to an existing program that is designed to be interdisciplinary or encourages interdisciplinary work, simply prepare your proposal in accordance with the program description or solicitation.

Frequently asked questions (FAQ)

1. does an interdisciplinary proposal have to be transformative.

No. The extent to which a proposed project is potentially transformative is just one of the considerations included in NSF's Intellectual Merit review criterion. See NSF's " Proposal and Award Policies and Procedures Guide " for more details.

2. Will interdisciplinary proposals be given preference when funding recommendations are made?

If a proposal is reviewed through an existing NSF program, this will depend on the program's criteria.

Some programs are specifically restricted to interdisciplinary research topics. In those programs, a great deal of weight is given to "interdisciplinary" aspects of the proposed work. Some other NSF programs, while not so restricted, explicitly encourage interdisciplinary research and consider it as a positive factor.

In programs that do not distinguish interdisciplinary research as a priority, the review will be based on the combined assessment of the project according to NSF's Merit Review criteria and any other special criteria that may be part of the program's solicitation or description. In these programs, interdisciplinary proposals that advance the program goals are encouraged and funded, and any "weight" is based on the anticipated potential of the project, not whether it is interdisciplinary or single-disciplinary in nature.

Finally, if a proposal is not reviewed through an existing program, it will be reviewed using the two NSF Merit Review criteria: Intellectual Merit and Broader Impacts.

3. Has NSF set aside funds for interdisciplinary research proposals?

Collaborations of interdisciplinary teams are encouraged throughout many NSF solicitations. For example, facility and center programs may call for interdisciplinary efforts.

In programs that do not explicitly call for interdisciplinary research, funds are not set aside for such proposals. However, a division, office or directorate may designate funds to support projects with noteworthy characteristics or potential, which could result from an interdisciplinary approach.

4. I discussed my ideas for an interdisciplinary proposal with several program officers but was discouraged to submit. What are my options?

Program officers play a critical role in providing guidance to the community on the various funding opportunities at NSF. You may have been discouraged to submit because your proposal is outside the scope of NSF’s programs and funding opportunities described in the " Proposal and Award Policies and Procedures Guide ."

Even if you are discouraged from submitting, you always retain the option to submit a proposal. To submit, you can contact one of the points of contact identified on this page, or you can contact NSF at [email protected] or (703) 292-4840. NSF's points of contact are responsible for finding an appropriate mechanism for reviewing your proposal.

5. Is the merit review process less receptive to interdisciplinary proposals?

No. Funding interdisciplinary research is a high priority for NSF and, in turn, program officers will identify appropriate panelists and ad hoc reviewers to ensure that the full range of interdisciplinary research is covered by a proposal's reviewers.

But it is important to remember that being interdisciplinary does not automatically make a proposal more worthy. Unfortunately, NSF must decline a high percentage of meritorious proposals for a variety of reasons.

NSF's program officers have the responsibility and authority to recommend awards for proposals that were not among the most highly ranked by the review panels in order to maintain a balanced portfolio of investments.

6. If my funded interdisciplinary research project is not successful in achieving its stated goals, will this jeopardize future funding possibilities?

As with any prior NSF award, reviewers are asked to comment on the quality of prior work when evaluating a proposal. Note that your proposal may contain up to five pages to describe those results.

7. May I submit the same interdisciplinary research proposal to more than one program concurrently?

No. As indicated in NSF's " Proposal and Award Policies and Procedures Guide ," you are required to select one applicable program announcement, solicitation or program description when preparing your proposal. In some instances, you can also select more than one of NSF's programs or units that you feel are appropriate to co-review your interdisciplinary research project.

Even if you submit your proposal to one program, an NSF program officer may elect to have your proposal reviewed by more than one program.

8. If my interdisciplinary research proposal is reviewed by more than one program, will it be subject to "double jeopardy"?

Preliminary analyses indicate that proposals that are co-reviewed by two or more programs actually have, in most cases, a slightly higher chance of being recommended for funding than do proposals reviewed in a single program.

9. May I add extra pages to the project description because my proposal is interdisciplinary?

No. Your proposal must conform to the " Proposal and Award Policies and Procedures Guide " or to the limitations specified in the program solicitation.

10. How will differing program target dates, deadlines or submission windows affect the review of an interdisciplinary proposal that is reviewed by multiple programs?

This may lengthen the review process somewhat if one program's submission cycle differs substantially from another's. The points of contact identified on this site will assure that an appropriate review is carried out, and program officers will work together to conduct these reviews as expeditiously as possible.

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Facilitating Interdisciplinary Research

Facilitating Interdisciplinary Research examines current interdisciplinary research efforts and recommends ways to stimulate and support such research.

Advances in science and engineering increasingly require the collaboration of scholars from various fields. This shift is driven by the need to address complex problems that cut across traditional disciplines, and the capacity of new technologies to both transform existing disciplines and generate new ones. At the same time, however, interdisciplinary research can be impeded by policies on hiring, promotion, tenure, proposal review, and resource allocation that favor traditional disciplines.

This report identifies steps that researchers, teachers, students, institutions, funding organizations, and disciplinary societies can take to more effectively conduct, facilitate, and evaluate interdisciplinary research programs and projects. Throughout the report key concepts are illustrated with case studies and results of the committee’s surveys of individual researchers and university provosts.

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Suggested Citation

National Academy of Sciences, National Academy of Engineering, and Institute of Medicine. 2005. Facilitating Interdisciplinary Research . Washington, DC: The National Academies Press. https://doi.org/10.17226/11153. Import this citation to: Bibtex EndNote Reference Manager

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All together now: how to write an interdisciplinary research proposal

Advice on drafting successful research proposals to secure support and funding for interdisciplinary projects, from three academics with experience developing research partnerships and collaborations

Catherine Healy

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Enter interdisciplinarity, or research drawing on two or more disciplines. It’s a term sometimes opportunistically applied to any project involving multiple subjects, but what it requires is the actual integration of knowledge from these fields – not just a few researchers working independently around a common interest.

Academic collaboration is rarely simple, and moving beyond disciplinary boundaries can throw up even more quandaries. How might political scientists work effectively with artists, say, or epidemiologists with historians? Will differences in training and methodology always be surmountable? Should one discipline take the lead over another?

Then there are the obstacles that come with transdisciplinarity, which incorporates both academic and societal partners. Working with civic organisations can involve having to negotiate very different expectations and communication styles, or even simply different levels of availability.

One of the most common dilemmas is how to go about preparing such research proposals.

Team members will understand what their own disciplines can contribute to the project, but how to make the case for a truly collaborative approach?

That’s one of the questions addressed by a major European Union-funded project championing interdisciplinarity.  SHAPE-ID provides tailored guidance for stakeholders interested in pursuing such partnerships, with a range of multimedia resources designed to guide you through the process. The following guidance comes from the project’s new online toolkit .

Ten tips for preparing interdisciplinary research proposals:

• Remember that your proposal will be read by a range of people, not just those within your research area: don’t assume knowledge of your discipline – describe (concisely) why your research is important, innovative and impactful.  
• Don’t just describe your proposal as interdisciplinary or transdisciplinary. Explain why an inter- or transdisciplinary approach is necessary to achieve the intended research outcomes.  
• Inter- and transdisciplinary proposals may be evaluated by those with expertise in collaborative research rather than your discipline. Write clearly for a general reader – use the minimum of technical language and abbreviations and define specialist terms when they are first used.  
• Collaboration and integration take many forms and work to achieve. Outline what kind(s) of collaboration or integration you envisage (theoretical, methodological, etc) and the specific ways in which you hope to achieve this.  
• Explain the methods that you plan to use and, if they are novel, give examples where they have been successfully used in other fields.  
• Bear in mind the extra costs associated with inter- or transdisciplinary research, such as additional network building, and budget for them accordingly.  
• If the research involves new collaborations, assume that these relationships will take time to develop and provide opportunities, both formal and informal, for this to happen, especially at the start of the project.  
• If the research includes different types of stakeholders, describe what they bring to the research, what and when they are expected to contribute, and how they will benefit from taking part.  
• Plan for a range of outputs to be produced across the lifetime of the project – this protects against failure and satisfies the needs of different collaborators. Academic publications are usually of little importance to societal partners who will often need more focused outputs – a programme of activities or a new tool to pilot – to justify their continued involvement in the project.  
• Criteria for authorship vary considerably across disciplines. Describe how authorships for project outputs will be allocated – or a process for agreeing on this early in the project – otherwise this can become a major problem.

Reflective questions to ask when planning an interdisciplinary research collaboration :

• Does everyone know who all the partners are and what they will bring to the project? Have all partners been able to contribute to the design of this project? Do the research questions frame the topic in ways that are engaging and fruitful for all partners? Has a shared understanding of the project’s aims and purpose been explicitly stated and agreed upon?  
• Is the research design predefined in the project proposal or to be negotiated among the group? Will there be opportunities for partners to learn about each other’s research methods and perspectives on the topic? How will you avoid making assumptions about partners’ approaches to the topic?  
• How will you ensure that no one group or perspective dominates the research process? Are there mechanisms in place to enable the ongoing exchange of ideas and opinions across disciplines and sectors? Will there be regular meetings that all project partners are expected to attend? Will all partners have the opportunity to provide feedback on project development and outputs? What happens if they cannot reach consensus?

It can be a tricky process, but the potential rewards of such collaborations are great . Getting these first steps right is crucial in paving the way to a successful outcome.

Catherine Healy recently completed an Irish Research Council-funded PhD at the Department of History at Trinity College Dublin .   Catherine Lyall and Isabel Fletcher of the University of Edinburgh are partners of SHAPE-ID, a project that brings together scholars from across Europe to examine interdisciplinary practices.

If you found this interesting and want advice and insight from academics and university staff delivered directly to your inbox each week,  sign up for the THE Campus newsletter .

SHAPE-ID has received funding from the EU’s Horizon 2020 research and innovation programme ,  under grant agreement number 822705 . The above guidance is just one example of the many multimedia resources available through the project’s online toolkit , which covers questions such as how to engage with collaborators from other disciplines, and how to support and assess these projects.

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Identifying interdisciplinary research in research projects

• Published: 24 August 2023
• Volume 128 , pages 5521–5544, ( 2023 )

Cite this article

• Hoang-Son Pham   ORCID: orcid.org/0000-0003-0349-3763 1 , 2 ,
• Bram Vancraeynest 1 , 2   na1 ,
• Hanne Poelmans 1 , 2 , 3   na1 ,
• Sadia Vancauwenbergh 1 , 2 , 3   na1 &
• Amr Ali-Eldin 1 , 2 , 4   na1  

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Identifying interdisciplinary research has become an important area of study in scientometrics. However, defining what exactly constitutes interdisciplinarity and how it manifests in research activities, such as publications or research projects, remains challenging. In this paper, we propose a mathematical modeling approach to interdisciplinarity measurement based on assessing project diversity. Particularly, we propose a novel approach that combines three indicators: the diversity of researchers, the diversity of research organizations, and the diversity of research disciplines involved in the project, to identify potentially interdisciplinary research projects. To measure diversity, we employ various methods, including distance matrix calculation, evaluation of the distance between researchers, and assessment of the relevancy of researchers’ expertise to the projects. We implemented the proposed approach on two datasets; FRIS and Dimensions. We could classify the interdisciplinarity of projects into three groups—Low, Medium, and High. Empirical results analysis supports the proposed approach assumption that the diversity of research projects gets higher when the distances between disciplines in the projects increase. Further, it was shown that the diversity of researchers and organizations was strongly affected by the distance. The number of researchers and organizations had a relatively small impact on the overall diversity score. Furthermore, the relevancy weight can be incorporated as an additional factor in the measurement of interdisciplinary.

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Acknowledgements

This study was supported by The Expertise Center for Research and Development Monitoring (ECOOM), Flanders, Belgium. The authors would like to acknowledge Dimensions for granting access to their database. Further, the authors would like to thank the reviewers for the feedback and comments that they have made to improve the paper.

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Bram Vancraeynest, Hanne Poelmans, Sadia Vancauwenbergh and Amr Ali-Eldin have contributed equally to this work.

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Centre for Research & Development Monitoring (ECOOM-UHasselt), 3500, Hasselt, Belgium

Hoang-Son Pham, Bram Vancraeynest, Hanne Poelmans, Sadia Vancauwenbergh & Amr Ali-Eldin

Data Science Institute, Hasselt University, 3500, Hasselt, Belgium

Directorate Research, Library, International Office, Hasselt University, 3500, Hasselt, Belgium

Hanne Poelmans & Sadia Vancauwenbergh

Computer engineering and Control systems Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt

Amr Ali-Eldin

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Pham, HS., Vancraeynest, B., Poelmans, H. et al. Identifying interdisciplinary research in research projects. Scientometrics 128 , 5521–5544 (2023). https://doi.org/10.1007/s11192-023-04810-6

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Research design: the methodology for interdisciplinary research framework

1 Biometris, Wageningen University and Research, PO Box 16, 6700 AA Wageningen, The Netherlands

Jarl K. Kampen

2 Statua, Dept. of Epidemiology and Medical Statistics, Antwerp University, Venusstraat 35, 2000 Antwerp, Belgium

Many of today’s global scientific challenges require the joint involvement of researchers from different disciplinary backgrounds (social sciences, environmental sciences, climatology, medicine, etc.). Such interdisciplinary research teams face many challenges resulting from differences in training and scientific culture. Interdisciplinary education programs are required to train truly interdisciplinary scientists with respect to the critical factor skills and competences. For that purpose this paper presents the Methodology for Interdisciplinary Research (MIR) framework. The MIR framework was developed to help cross disciplinary borders, especially those between the natural sciences and the social sciences. The framework has been specifically constructed to facilitate the design of interdisciplinary scientific research, and can be applied in an educational program, as a reference for monitoring the phases of interdisciplinary research, and as a tool to design such research in a process approach. It is suitable for research projects of different sizes and levels of complexity, and it allows for a range of methods’ combinations (case study, mixed methods, etc.). The different phases of designing interdisciplinary research in the MIR framework are described and illustrated by real-life applications in teaching and research. We further discuss the framework’s utility in research design in landscape architecture, mixed methods research, and provide an outlook to the framework’s potential in inclusive interdisciplinary research, and last but not least, research integrity.

Introduction

Current challenges, e.g., energy, water, food security, one world health and urbanization, involve the interaction between humans and their environment. A (mono)disciplinary approach, be it a psychological, economical or technical one, is too limited to capture any one of these challenges. The study of the interaction between humans and their environment requires knowledge, ideas and research methodology from different disciplines (e.g., ecology or chemistry in the natural sciences, psychology or economy in the social sciences). So collaboration between natural and social sciences is called for (Walsh et al. 1975 ).

Over the past decades, different forms of collaboration have been distinguished although the terminology used is diverse and ambiguous. For the present paper, the term interdisciplinary research is used for (Aboelela et al. 2007 , p. 341):

any study or group of studies undertaken by scholars from two or more distinct scientific disciplines. The research is based upon a conceptual model that links or integrates theoretical frameworks from those disciplines, uses study design and methodology that is not limited to any one field, and requires the use of perspectives and skills of the involved disciplines throughout multiple phases of the research process.

Scientific disciplines (e.g., ecology, chemistry, biology, psychology, sociology, economy, philosophy, linguistics, etc.) are categorized into distinct scientific cultures: the natural sciences, the social sciences and the humanities (Kagan 2009 ). Interdisciplinary research may involve different disciplines within a single scientific culture, and it can also cross cultural boundaries as in the study of humans and their environment.

A systematic review of the literature on natural-social science collaboration (Fischer et al. 2011 ) confirmed the general impression of this collaboration to be a challenge. The nearly 100 papers in their analytic set mentioned more instances of barriers than of opportunities (72 and 46, respectively). Four critical factors for success or failure in natural-social science collaboration were identified: the paradigms or epistemologies in the current (mono-disciplinary) sciences, the skills and competences of the scientists involved, the institutional context of the research, and the organization of collaborations (Fischer et al. 2011 ). The so-called “paradigm war” between neopositivist versus constructivists within the social and behavioral sciences (Onwuegbuzie and Leech 2005 ) may complicate pragmatic collaboration further.

It has been argued that interdisciplinary education programs are required to train truly interdisciplinary scientists with respect to the critical factor skills and competences (Frischknecht 2000 ) and accordingly, some interdisciplinary programs have been developed since (Baker and Little 2006 ; Spelt et al. 2009 ). The overall effect of interdisciplinary programs can be expected to be small as most programs are mono-disciplinary and based on a single paradigm (positivist-constructivist, qualitative-quantitative; see e.g., Onwuegbuzie and Leech 2005 ). We saw in our methodology teaching, consultancy and research practices working with heterogeneous groups of students and staff, that most had received mono-disciplinary training with a minority that had received multidisciplinary training, with few exceptions within the same paradigm. During our teaching and consultancy for heterogeneous groups of students and staff aimed at designing interdisciplinary research, we built the framework for methodology in interdisciplinary research (MIR). With the MIR framework, we aspire to contribute to the critical factors skills and competences (Fischer et al. 2011 ) for social and natural sciences collaboration. Note that the scale of interdisciplinary research projects we have in mind may vary from comparably modest ones (e.g., finding a link between noise reducing asphalt and quality of life; Vuye et al. 2016 ) to very large projects (finding a link between anthropogenic greenhouse gas emissions, climate change, and food security; IPCC 2015 ).

In the following section of this paper we describe the MIR framework and elaborate on its components. The third section gives two examples of the application of the MIR framework. The paper concludes with a discussion of the MIR framework in the broader contexts of mixed methods research, inclusive research, and other promising strains of research.

The methodology in interdisciplinary research framework

Research as a process in the methodology in interdisciplinary research framework.

The Methodology for Interdisciplinary Research (MIR) framework was built on the process approach (Kumar 1999 ), because in the process approach, the research question or hypothesis is leading for all decisions in the various stages of research. That means that it helps the MIR framework to put the common goal of the researchers at the center, instead of the diversity of their respective backgrounds. The MIR framework also introduces an agenda: the research team needs to carefully think through different parts of the design of their study before starting its execution (Fig.  1 ). First, the team discusses the conceptual design of their study which contains the ‘why’ and ‘what’ of the research. Second, the team discusses the technical design of the study which contains the ‘how’ of the research. Only after the team agrees that the complete research design is sufficiently crystalized, the execution of the work (including fieldwork) starts.

The Methodology of Interdisciplinary Research framework

Whereas the conceptual and technical designs are by definition interdisciplinary team work, the respective team members may do their (mono)disciplinary parts of fieldwork and data analysis on a modular basis (see Bruns et al. 2017 : p. 21). Finally, when all evidence is collected, an interdisciplinary synthesis of analyses follows which conclusions are input for the final report. This implies that the MIR framework allows for a range of scales of research projects, e.g., a mixed methods project and its smaller qualitative and quantitative modules, or a multi-national sustainability project and its national sociological, economic and ecological modules.

The conceptual design

Interdisciplinary research design starts with the “conceptual design” which addresses the ‘why’ and ‘what’ of a research project at a conceptual level to ascertain the common goals pivotal to interdisciplinary collaboration (Fischer et al. 2011 ). The conceptual design includes mostly activities such as thinking, exchanging interdisciplinary knowledge, reading and discussing. The product of the conceptual design is called the “conceptual frame work” which comprises of the research objective (what is to be achieved by the research), the theory or theories that are central in the research project, the research questions (what knowledge is to be produced), and the (partial) operationalization of constructs and concepts that will be measured or recorded during execution. While the members of the interdisciplinary team and the commissioner of the research must reach a consensus about the research objective, the ‘why’, the focus in research design must be the production of the knowledge required to achieve that objective the ‘what’.

With respect to the ‘why’ of a research project, an interdisciplinary team typically starts with a general aim as requested by the commissioner or funding agency, and a set of theories to formulate a research objective. This role of theory is not always obvious to students from the natural sciences, who tend to think in terms of ‘models’ with directly observable variables. On the other hand, students from the social sciences tend to think in theories with little attention to observable variables. In the MIR framework, models as simplified descriptions or explanations of what is studied in the natural sciences play the same role in informing research design, raising research questions, and informing how a concept is understood, as do theories in social science.

Research questions concern concepts, i.e. general notions or ideas based on theory or common sense that are multifaceted and not directly visible or measurable. For example, neither food security (with its many different facets) nor a person’s attitude towards food storage may be directly observed. The operationalization of concepts, the transformation of concepts into observable indicators, in interdisciplinary research requires multiple steps, each informed by theory. For instance, in line with particular theoretical frameworks, sustainability and food security may be seen as the composite of a social, an economic and an ecological dimension (e.g., Godfray et al. 2010 ).

As the concept of interest is multi-disciplinary and multi-dimensional, the interdisciplinary team will need to read, discuss and decide on how these dimensions and their indicators are weighted to measure the composite interdisciplinary concept to get the required interdisciplinary measurements. The resulting measure or measures for the interdisciplinary concept may be of the nominal, ordinal, interval and ratio level, or a combination thereof. This operationalization procedure is known as the port-folio approach to widely defined measurements (Tobi 2014 ). Only after the research team has finalized the operationalization of the concepts under study, the research questions and hypotheses can be made operational. For example, a module with descriptive research questions may now be turned into an operational one like, what are the means and variances of X1, X2, and X3 in a given population? A causal research question may take on the form, is X (a composite of X1, X2 and X3) a plausible cause for the presence or absence of Y? A typical qualitative module could study, how do people talk about X1, X2 and X3 in their everyday lives?

The technical design

Members of an interdisciplinary team usually have had different training with respect to research methods, which makes discussing and deciding on the technical design more challenging but also potentially more creative than in a mono-disciplinary team. The technical design addresses the issues ‘how, where and when will research units be studied’ (study design), ‘how will measurement proceed’ (instrument selection or design), ‘how and how many research units will be recruited’ (sampling plan), and ‘how will collected data be analyzed and synthesized’ (analysis plan). The MIR framework provides the team a set of topics and their relationships to one another and to generally accepted quality criteria (see Fig.  1 ), which helps in designing this part of the project.

Interdisciplinary teams need be pragmatic as the research questions agreed on are leading in decisions on the data collection set-up (e.g., a cross-sectional study of inhabitants of a region, a laboratory experiment, a cohort study, a case control study, etc.), the so-called “study design” (e.g., Kumar 2014 ; De Vaus 2001 ; Adler and Clark 2011 ; Tobi and van den Brink 2017 ) instead of traditional ‘pet’ approaches. Typical study designs for descriptive research questions and research questions on associations are the cross-sectional study design. Longitudinal study designs are required to investigate development over time and cause-effect relationships ideally are studied in experiments (e.g., Kumar 2014 ; Shipley 2016 ). Phenomenological questions concern a phenomenon about which little is known and which has to be studied in the environment where it takes place, which calls for a case study design (e.g., Adler and Clark 2011 : p. 178). For each module, the study design is to be further explicated by the number of data collection waves, the level of control by the researcher and its reference period (e.g., Kumar 2014 ) to ensure the teams common understanding.

Then, decisions about the way data is to be collected, e.g., by means of certified instruments, observation, interviews, questionnaires, queries on existing data bases, or a combination of these are to be made. It is especially important to discuss the role of the observer (researcher) as this is often a source of misunderstanding in interdisciplinary teams. In the sciences, the observer is usually considered a neutral outsider when reading a standardized measurement instrument (e.g., a pyranometer to measure incoming solar radiation). In contrast, in the social sciences, the observer may be (part of) the measurement instrument, for example in participant observation or when doing in-depth interviews. After all, in participant observation the researcher observes from a member’s perspective and influences what is observed owing to the researcher’s participation (Flick 2006 : p. 220). Similarly in interviews, by which we mean “a conversation that has a structure and a purpose determined by the one party—the interviewer” (Kvale 2007 : p. 7), the interviewer and the interviewee are part of the measurement instrument (Kvale and Brinkmann 2009 : p. 2). In on-line and mail questionnaires the interviewer is eliminated as part of the instrument by standardizing the questions and answer options. Queries on existing data bases refer to the use of secondary data or secondary analysis. Different disciplines tend to use different bibliographic data bases (e.g., CAB Abstracts, ABI/INFORM or ERIC) and different data repositories (e.g., the European Social Survey at europeansocialsurvey.org or the International Council for Science data repository hosted by www.pangaea.de ).

Depending on whether or not the available, existing, measurement instruments tally with the interdisciplinary operationalisations from the conceptual design, the research team may or may not need to design instruments. Note that in some cases the social scientists’ instinct may be to rely on a questionnaire whereas the collaboration with another discipline may result in more objective possibilities (e.g., compare asking people about what they do with surplus medication, versus measuring chemical components from their input into the sewer system). Instrument design may take on different forms, such as the design of a device (e.g., pyranometer), a questionnaire (Dillman 2007 ) or a part thereof (e.g., a scale see DeVellis 2012 ; Danner et al. 2016 ), an interview guide with topics or questions for the interviewees, or a data extraction form in the context of secondary analysis and literature review (e.g., the Cochrane Collaboration aiming at health and medical sciences or the Campbell Collaboration aiming at evidence based policies).

Researchers from different disciplines are inclined to think of different research objects (e.g., animals, humans or plots), which is where the (specific) research questions come in as these identify the (possibly different) research objects unambiguously. In general, research questions that aim at making an inventory, whether it is an inventory of biodiversity or of lodging, call for a random sampling design. Both in the biodiversity and lodging example, one may opt for random sampling of geographic areas by means of a list of coordinates. Studies that aim to explain a particular phenomenon in a particular context would call for a purposive sampling design (non-random selection). Because studies of biodiversity and housing obey the same laws in terms of appropriate sampling design for similar research questions, individual students and researchers are sensitized to commonalities of their respective (mono)disciplines. For example, a research team interested in the effects of landslides on a socio-ecological system may select for their study one village that suffered from landslides and one village that did not suffer from landslides that have other characteristics in common (e.g., kind of soil, land use, land property legislation, family structure, income distribution, et cetera).

The data analysis plan describes how data will be analysed, for each of the separate modules and for the project at large. In the context of a multi-disciplinary quantitative research project, the data analysis plan will list the intended uni-, bi- and multivariate analyses such as measures for distributions (e.g., means and variances), measures for association (e.g., Pearson Chi square or Kendall Tau) and data reduction and modelling techniques (e.g., factor analysis and multiple linear regression or structural equation modelling) for each of the research modules using the data collected. When applicable, it will describe interim analyses and follow-up rules. In addition to the plans at modular level, the data analysis plan must describe how the input from the separate modules, i.e. different analyses, will be synthesized to answer the overall research question. In case of mixed methods research, the particular type of mixed methods design chosen describes how, when, and to what extent the team will synthesize the results from the different modules.

Unfortunately, in our experience, when some of the research modules rely on a qualitative approach, teams tend to refrain from designing a data analysis plan before starting the field work. While absence of a data analysis plan may be regarded acceptable in fields that rely exclusively on qualitative research (e.g., ethnography), failure to communicate how data will be analysed and what potential evidence will be produced posits a deathblow to interdisciplinarity. For many researchers not familiar with qualitative research, the black box presented as “qualitative data analysis” is a big hurdle, and a transparent and systematic plan is a sine qua non for any scientific collaboration. The absence of a data analysis plan for all modules results in an absence of synthesis of perspectives and skills of the disciplines involved, and in separate (disciplinary) research papers or separate chapters in the research report without an answer to the overall research question. So, although researchers may find it hard to write the data analysis plan for qualitative data, it is pivotal in interdisciplinary research teams.

Similar to the quantitative data analysis plan, the qualitative data analysis plan presents the description of how the researcher will get acquainted with the data collected (e.g., by constructing a narrative summary per interviewee or a paired-comparison of essays). Additionally, the rules to decide on data saturation need be presented. Finally, the types of qualitative analyses are to be described in the data analysis plan. Because there is little or no standardized terminology in qualitative data analysis, it is important to include a precise description as well as references to the works that describe the method intended (e.g., domain analysis as described by Spradley 1979 ; or grounded theory by means of constant-comparison as described by Boeije 2009 ).

Integration

To benefit optimally from the research being interdisciplinary the modules need to be brought together in the integration stage. The modules may be mono- or interdisciplinary and may rely on quantitative, qualitative or mixed methods approaches. So the MIR framework fits the view that distinguishes three multimethods approaches (quali–quali, quanti–quanti, and quali–quant).

Although the MIR framework has not been designed with the intention to promote mixed methods research, it is suitable for the design of mixed methods research as the kind of research that calls for both quantitative and qualitative components (Creswell and Piano Clark 2011 ). Indeed, just like the pioneers in mixed methods research (Creswell and Piano Clark 2011 : p. 2), the MIR framework deconstructs the package deals of paradigm and data to be collected. The synthesis of the different mono or interdisciplinary modules may benefit from research done on “the unique challenges and possibilities of integration of qualitative and quantitative approaches” (Fetters and Molina-Azorin 2017 : p. 5). We distinguish (sub) sets of modules being designed as convergent, sequential or embedded (adapted from mixed methods design e.g., Creswell and Piano Clark 2011 : pp. 69–70). Convergent modules, whether mono or interdisciplinary, may be done parallel and are integrated after completion. Sequential modules are done after one another and the first modules inform the latter ones (this includes transformative and multiphase mixed methods design). Embedded modules are intertwined. Here, modules depend on one another for data collection and analysis, and synthesis may be planned both during and after completion of the embedded modules.

Scientific quality and ethical considerations in the design of interdisciplinary research

A minimum set of jargon related to the assessment of scientific quality of research (e.g., triangulation, validity, reliability, saturation, etc.) can be found scattered in Fig.  1 . Some terms are reserved by particular paradigms, others may be seen in several paradigms with more or less subtle differences in meaning. In the latter case, it is important that team members are prepared to explain and share ownership of the term and respect the different meanings. By paying explicit attention to the quality concepts, researchers from different disciplines learn to appreciate each other’s concerns for good quality research and recognize commonalities. For example, the team may discuss measurement validity of both a standardized quantitative instrument and that of an interview and discover that the calibration of the machine serves a similar purpose as the confirmation of the guarantee of anonymity at the start of an interview.

Throughout the process of research design, ethics require explicit discussion among all stakeholders in the project. Ethical issues run through all components in the MIR framework in Fig.  1 . Where social and medical scientists may be more sensitive to ethical issues related to humans (e.g., the 1979 Belmont Report criteria of beneficence, justice, and respect), others may be more sensitive to issues related to animal welfare, ecology, legislation, the funding agency (e.g., implications for policy), data and information sharing (e.g., open access publishing), sloppy research practices, or long term consequences of the research. This is why ethics are an issue for the entire interdisciplinary team and cannot be discussed on project module level only.

The MIR framework in practice: two examples

Teaching research methodology to heterogeneous groups of students, institutional context and background of the mir framework.

Wageningen University and Research (WUR) advocates in its teaching and research an interdisciplinary approach to the study of global issues related to the motto “To explore the potential of nature to improve the quality of life.” Wageningen University’s student population is multidisciplinary and international (e.g., Tobi and Kampen 2013 ). Traditionally, this challenge of diversity in one classroom is met by covering a width of methodological topics and examples from different disciplines. However, when students of various programmes received methodological education in mixed classes, students of some disciplines would regard with disinterest or even disdain methods and techniques of the other disciplines. Different disciplines, especially from the qualitative respectively quantitative tradition in the social sciences (Onwuegbuzie and Leech 2005 : p. 273), claim certain study designs, methods of data collection and analysis as their territory, a claim reflected in many textbooks. We found that students from a qualitative tradition would not be interested, and would not even study, content like the design of experiments and quantitative data collection; and students from a quantitative tradition would ignore case study design and qualitative data collection. These students assumed they didn’t need any knowledge about ‘the other tradition’ for their future careers, despite the call for interdisciplinarity.

To enhance interdisciplinarity, WUR provides an MSc course mandatory for most students, in which multi-disciplinary teams do research for a commissioner. Students reported difficulties similar to the ones found in the literature: miscommunication due to talking different scientific languages and feelings of distrust and disrespect due to prejudice. This suggested that research methodology courses ought help prepare for interdisciplinary collaboration by introducing a single methodological framework that 1) creates sensitivity to the pros and challenges of interdisciplinary research by means of a common vocabulary and fosters respect for other disciplines, 2) starts from the research questions as pivotal in decision making on research methods instead of tradition or ontology, and 3) allows available methodologies and methods to be potentially applicable to any scientific research problem.

Teaching with MIR—the conceptual framework

As a first step, we replaced textbooks by ones refusing the idea that any scientific tradition has exclusive ownership of any methodological approach or method. The MIR framework further guides our methodology teaching in two ways. First, it presents a logical sequence of topics (first conceptual design, then technical design; first research question(s) or hypotheses, then study design; etc.). Second, it allows for a conceptual separation of topics (e.g., study design from instrument design). Educational programmes at Wageningen University and Research consistently stress the vital importance of good research design. In fact, 50% of the mark in most BSc and MSc courses in research methodology is based on the assessment of a research proposal that students design in small (2-4 students) and heterogeneous (discipline, gender and nationality) groups. The research proposal must describe a project which can be executed in practice, and which limitations (measurement, internal, and external validity) are carefully discussed.

Groups start by selecting a general research topic. They discuss together previously attained courses from a range of programs to identify personal and group interests, with the aim to reach an initial research objective and a general research question as input for the conceptual design. Often, their initial research objective and research question are too broad to be researchable (e.g., Kumar 2014 : p. 64; Adler and Clark 2011 : p. 71). In plenary sessions, the (basics of) critical assessment of empirical research papers is taught with special attention to the ‘what’ and ‘why’ section of research papers. During tutorials students generate research questions until the group agrees on a research objective, with one general research question that consists of a small set of specific research questions. Each of the specific research questions may stem from a different discipline, whereas answering the general research question requires integrating the answers to all specific research questions.

The group then identifies the key concepts in their research questions, while exchanging thoughts on possible attributes based on what they have learnt from previous courses (theories) and literature. When doing so they may judge the research question as too broad, in which case they will turn to the question strategies toolbox again. Once they agree on the formulation of the research questions and the choice of concepts, tasks are divided. In general, each student turns to the literature he/she is most familiar with or interested in, for the operationalization of the concept into measurable attributes and writes a paragraph or two about it. In the next meeting, the groups read and discuss the input and decide on the set-up and division of tasks with respect to the technical design.

Teaching with MIR—the technical framework

The technical part of research design distinguishes between study design, instrument design, sampling design, and the data analysis plan. In class, we first present students with a range of study designs (cross sectional, experimental, etc.). Student groups select an appropriate study design by comparing the demands made by the research questions with criteria for internal validity. When a (specific) research question calls for a study design that is not seen as practically feasible or ethically possible, they will rephrase the research question until the demands of the research question tally with the characteristics of at least one ethical, feasible and internally valid study design.

While following plenary sessions during which different random and non-random sampling or selection strategies are taught, groups start working on their sampling design. The groups make two decisions informed by their research question: the population(s) of research units, and the requirements of the sampling strategy for each population. Like many other aspects in research design, this can be an iterative process. For example, suppose the research question mentioned “local policy makers,” which is too vague for a sampling design. Then the decision may be to limit the study to “policy makers at the municipality level in the Netherlands” and adapt the general and the specific research questions accordingly. Next, the group identifies whether a sample design needs to focus on diversity (e.g., when the objective is to make an inventory of possible local policies), representativeness (e.g., when the objective is to estimate prevalence of types of local policies), or people with particular information (e.g., when the objective is to study people having experience with a given local policy). When a sample has to representative, the students must produce an assessment of external validity, whereas when the aim is to map diversity the students must discuss possible ways of source triangulation. Finally, in conjunction with the data analysis plan, students decide on the sample size and/or the saturation criteria.

When the group has agreed on their population(s) and the strategy for recruiting research units, the next step is to finalize the technical aspects of operationalisation i.e. addressing the issue of exactly how information will be extracted from the research units. Depending on what is practically feasible qua measurement, the choice of a data collection instrument may be a standardised (e.g., a spectrograph, a questionnaire) or less standardised (e.g., semi-structured interviews, visual inspection) one. The students have to discuss the possibilities of method triangulation, and explain the possible weaknesses of their data collection plan in terms of measurement validity and reliability.

Recent developments

Presently little attention is payed to the data analysis plan, procedures for synthesis and reporting because the programmes differ on their offer in data analysis courses, and because execution of the research is not part of the BSc and MSc methodology courses. Recently, we have designed one course for an interdisciplinary BSc program in which the research question is put central in learning and deciding on statistics and qualitative data analysis. Nonetheless, during the past years the number of methodology courses for graduate students that supported the MIR framework have been expanded, e.g., a course “From Topic to Proposal”; separate training modules on questionnaire construction, interviewing, and observation; and optional courses on quantitative and qualitative data analysis. These courses are open to (and attended by) PhD students regardless of their program. In Flanders (Belgium), the Flemish Training Network for Statistics and Methodology (FLAMES) has for the last four years successfully applied the approach outlined in Fig.  1 in its courses for research design and data collection methods. The division of the research process in terms of a conceptual design, technical design, operationalisation, analysis plan, and sampling plan, has proved to be appealing for students of disciplines ranging from linguistics to bioengineering.

Researching with MIR: noise reducing asphalt layers and quality of life

Research objective and research question.

This example of the application of the MIR framework comes from a study about the effects of “noise reducing asphalt layers” on the quality of life (Vuye et al. 2016 ), a project commissioned by the City of Antwerp in 2015 and executed by a multidisciplinary research team of Antwerp University (Belgium). The principal researcher was an engineer from the Faculty of Applied Engineering (dept. Construction), supported by two researchers from the Faculty of Medicine and Health Sciences (dept. of Epidemiology and Social Statistics), one with a background in qualitative and one with a background in quantitative research methods. A number of meetings were held where the research team and the commissioners discussed the research objective (the ‘what’ and ‘why’).The research objective was in part dictated by the European Noise Directive 2002/49/EC, which forces all EU member states to draft noise action plans, and the challenge in this study was to produce evidence of a link between the acoustic and mechanical properties of different types of asphalt, and the quality of life of people living in the vicinity of the treated roads. While there was literature available about the effects of road surface on sound, and other studies had studied the link between noise and health, no study was found that produced evidence simultaneously about noise levels of roads and quality of life. The team therefore decided to test the hypothesis that traffic noise reduction has a beneficial effect on the quality of life of people into the central research. The general research question was, “to what extent does the placing of noise reducing asphalt layers increase the quality of life of the residents?”

Study design

In order to test the effect of types of asphalt, initially a pretest–posttest experiment was designed, which was expanded by several added experimental (change of road surface) and control (no change of road surface) groups. The research team gradually became aware that quality of life may not be instantly affected by lower noise levels, and that a time lag is involved. A second posttest aimed to follow up on this effect although it could only be implemented in a selection of experimental sites.

Instrument selection and design

Sound pressure levels were measured by an ISO-standardized procedure called the Statistical Pass-By (SPB) method. A detailed description of the method is in Vuye et al. ( 2016 ). No such objective procedure is available for measuring quality of life, which can only be assessed by self-reports of the residents. Some time was needed for the research team to accept that measuring a multidimensional concept like quality of life is more complicated than just having people rate their “quality of life” on a 10 point scale. For instance, questions had to be phrased in a way that gave not away the purpose of the research (Hawthorne effect), leading to the inclusion of questions about more nuisances than traffic noise alone. This led to the design of a self-administered questionnaire, with questions of Flanders Survey on Living Environment (Departement Leefmilieu, Natuur & Energie 2013 ) appended by new questions. Among other things, the questionnaire probed for experienced nuisance by sound, quality of sleep, effort to concentrate, effort to have a conversation inside or outside the home, physical complaints such as headaches, etc.

Sampling design

The selected sites needed to accommodate both types of measurements: that of noise from traffic and quality of life of residents. This was a complicating factor that required several rounds of deliberation. While countrywide only certain roads were available for changing the road surface, these roads had to be mutually comparable in terms of the composition of the population, type of residential area (e.g., reports from the top floor of a tall apartment building cannot be compared to those at ground level), average volume of traffic, vicinity of hospitals, railroads and airports, etc. At the level of roads therefore, targeted sampling was applied, whereas at the level of residents the aim was to realize a census of all households within a given perimeter from the treated road surfaces. Considerations about the reliability of applied instruments were guiding decisions with respect to sampling. While the measurements of the SPB method were sufficiently reliable to allow for relatively few measurements, the questionnaire suffered from considerable nonresponse which hampered statistical power. It was therefore decided to increase the power of the study by adding control groups in areas where the road surface was not replaced. This way, detecting an effect of the intervention did not solely depend on the turnout of the pre and the post-test.

Data analysis plan

The statistical analysis had to account for the fact that data were collected at two different levels: the level of the residents filling out the questionnaires, and the level of the roads which surface was changed. Because survey participation was confidential, results of the pre- and posttest could only be compared at aggregate (street) level. The analysis had to control for confounding variables (e.g., sample composition, variety in traffic volume, etc.), experimental factors (varieties in experimental conditions, and controls), and non-normal dependent variables. The statistical model appropriate for analysis of such data is a Generalised Linear Mixed Model.

Data were collected during the course of 2015, 2016 and 2017 and are awaiting final analysis in Spring 2017. Intermediate analyses resulted in several MSc theses, conference presentations, and working papers that reported on parts of the research.

In this paper we presented the Methodology in Interdisciplinary Research framework that we developed over the past decade building on our experience as lecturers, consultants and researchers. The MIR framework recognizes research methodology and methods as important content in the critical factor skills and competences. It approaches research and collaboration as a process that needs to be designed with the sole purpose to answer the general research question. For the conceptual design the team members have to discuss and agree on the objective of their communal efforts without squeezing it into one single discipline and, thus, ignoring complexity. The specific research questions, when formulated, contribute to (self) respect in collaboration as they represent and stand witness of the need for interdisciplinarity. In the technical design, different parts were distinguished to stimulate researchers to think and design research out of their respective disciplinary boxes and consider, for example, an experimental design with qualitative data collection, or a case study design based on quantitative information.

In our teaching and consultancy, we first developed a MIR framework for social sciences, economics, health and environmental sciences interdisciplinarity. It was challenged to include research in the design discipline of landscape architecture. What characterizes research in landscape architecture and other design principles, is that the design product as well as the design process may be the object of study. Lenzholder et al. ( 2017 ) therefore distinguish three kinds of research in landscape architecture. The first kind, “Research into design” studies the design product post hoc and the MIR framework suits the interdisciplinary study of such a product. In contrast, “Research for design” generates knowledge that feeds into the noun and the verb ‘design’, which means it precedes the design(ing). The third kind, Research through Design(ing) employs designing as a research method. At first, just like Deming and Swaffield ( 2011 ), we were a bit skeptical about “designing” as a research method. Lenzholder et al. ( 2017 ) pose that the meaning of research through design has evolved through a (neo)positivist, constructivist and transformative paradigm to include a pragmatic stance that resembles the pragmatic stance assumed in the MIR framework. We learned that, because landscape architecture is such an interdisciplinary field, the process approach and the distinction between a conceptual and technical research design was considered very helpful and embraced by researchers in landscape architecture (Tobi and van den Brink 2017 ).

Mixed methods research (MMR) has been considered to study topics as diverse as education (e.g., Powell et al. 2008 ), environmental management (e.g., Molina-Azorin and Lopez-Gamero 2016 ), health psychology (e.g., Bishop 2015 ) and information systems (e.g., Venkatesh et al. 2013 ). Nonetheless, the MIR framework is the first to put MMR in the context of integrating disciplines beyond social inquiry (Greene 2008 ). The splitting of the research into modules stimulates the identification and recognition of the contribution of both distinct and collaborating disciplines irrespective of whether they contribute qualitative and/or quantitative research in the interdisciplinary research design. As mentioned in Sect.  2.4 the integration of the different research modules in one interdisciplinary project design may follow one of the mixed methods designs. For example, we witnessed at several occasions the integration of social and health sciences in interdisciplinary teams opting for sequential modules in a sequential exploratory mixed methods fashion (e.g., Adamson 2005 : 234). In sustainability science research, we have seen the design of concurrent modules for a concurrent nested mixed methods strategy (ibid) in research integrating the social and natural sciences and economics.

The limitations of the MIR framework are those of any kind of collaboration: it cannot work wonders in the absence of awareness of the necessity and it requires the willingness to work, learn, and research together. We developed MIR framework in and alongside our own teaching, consultancy and research, it has not been formally evaluated and compared in an experiment with teaching, consultancy and research with, for example, the regulative cycle for problem solving (van Strien 1986 ), or the wheel of science from Babbie ( 2013 ). In fact, although we wrote “developed” in the previous sentence, we are fully aware of the need to further develop and refine the framework as is.

The importance of the MIR framework lies in the complex, multifaceted nature of issues like sustainability, food security and one world health. For progress in the study of these pressing issues the understanding, construction and quality of interdisciplinary portfolio measurements (Tobi 2014 ) are pivotal and require further study as well as procedures facilitating the integration across different disciplines.

Another important strain of further research relates to the continuum of Responsible Conduct of Research (RCR), Questionable Research Practices (QRP), and deliberate misconduct (Steneck 2006 ). QRP includes failing to report all of a study’s conditions, stopping collecting data earlier than planned because one found the result one had been looking for, etc. (e.g., John et al. 2012 ; Simmons et al. 2011 ; Kampen and Tamás 2014 ). A meta-analysis on selfreports obtained through surveys revealed that about 2% of researchers had admitted to research misconduct at least once, whereas up to 33% admitted to QRPs (Fanelli 2009 ). While the frequency of QRPs may easily eclipse that of deliberate fraud (John et al. 2012 ) these practices have received less attention than deliberate misconduct. Claimed research findings may often be accurate measures of the prevailing biases and methodological rigor in a research field (Fanelli and Ioannidis 2013 ; Fanelli 2010 ). If research misconduct and QRP are to be understood then the disciplinary context must be grasped as a locus of both legitimate and illegitimate activity (Fox 1990 ). It would be valuable to investigate how working in interdisciplinary teams and, consequently, exposure to other standards of QRP and RCR influence research integrity as the appropriate research behavior from the perspective of different professional standards (Steneck 2006 : p. 56). These differences in scientific cultures concern criteria for quality in design and execution of research, reporting (e.g., criteria for authorship of a paper, preferred publication outlets, citation practices, etc.), archiving and sharing of data, and so on.

Other strains of research include interdisciplinary collaboration and negotiation, where we expect contributions from the “science of team science” (Falk-Krzesinski et al. 2010 ); and compatibility of the MIR framework with new research paradigms such as “inclusive research” (a mode of research involving people with intellectual disabilities as more than just objects of research; e.g., Walmsley and Johnson 2003 ). Because of the complexity and novelty of inclusive health research a consensus statement was developed on how to conduct health research inclusively (Frankena et al., under review). The eight attributes of inclusive health research identified may also be taken as guiding attributes in the design of inclusive research according to the MIR framework. For starters, there is the possibility of inclusiveness in the conceptual framework, particularly in determining research objectives, and in discussing possible theoretical frameworks with team members with an intellectual disability which Frankena et al. labelled the “Designing the study” attribute. There are also opportunities for inclusiveness in the technical design, and in execution. For example, the inclusiveness attribute “generating data” overlaps with the operationalization and measurement instrument design/selection and the attribute “analyzing data” aligns with the data analysis plan in the technical design.

On a final note, we hope to have aroused the reader’s interest in, and to have demonstrated the need for, a methodology for interdisciplinary research design. We further hope that the MIR framework proposed and explained in this article helps those involved in designing an interdisciplinary research project to get a clearer view of the various processes that must be secured during the project’s design and execution. And we look forward to further collaboration with scientists from all cultures to contribute to improving the MIR framework and make interdisciplinary collaborations successful.

Acknowledgements

The MIR framework is the result of many discussions with students, researchers and colleagues, with special thanks to Peter Tamás, Jennifer Barrett, Loes Maas, Giel Dik, Ruud Zaalberg, Jurian Meijering, Vanessa Torres van Grinsven, Matthijs Brink, Gerda Casimir, and, last but not least, Jenneken Naaldenberg.

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This searchable database contains details of pump-primed cross-discipline research projects taking place at the University of Aberdeen.

One of the main strands of our Aberdeen 2040 plan is to address urgent and wide-ranging challenges in the interdisciplinary areas of Energy Transition, Social Inclusion and Cultural Diversity, Environment and Biodiversity, Data and Artificial Intelligence, and Health, Nutrition and Wellbeing.

The wide range of projects on display here is a demonstration of some of the early-stage research being funded in these critical areas as well as the University's commitment to addressing the UN's 17 Sustainable Development Goals, a "blueprint to achieve a better and more sustainable future for all."

Projects undertaken in 2021 were funded by the University of Aberdeen's Scottish Funding Council allocation.

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In their own words: the democracy Tunisians want

Initiating collaborative work on radiofrequency over optics for FFC imaging

A holistic approach towards identifying the phenotypic plasticity and resilience response to environmental stress in the intertidal zone

Scotland on fire: towards a taphonomy of cremation

Human robot interaction network

An interdisciplinary approach to generate a paradigm shift in characterising biodiversity

Analysing stakeholder power dynamics in the policy-making process: an exploratory analysis of Scottish cities and towns

Pathways to sustainable and healthy diets: Young people’s views about climate change and eating less meat

Transcriptomic profiling of viral infections in the brain of a key pollinator

Citizen science for biodiversity conservation in agricultural-conservation transition zones

University of South Florida

Social Work Interdisciplinary Research Lab (SWIRL)

School of Social Work

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Social work interdisciplinary research lab | school of social work | cbcs | university of south florida.

What is SWIRL?

Our Approach & Services

Decade of Study Abroad

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Connecting Across Disciplines in PBL

Here are three ways to set up project-based learning that involves more than one subject area, which is highly engaging for students.

When seventh-grade students in Alexandria, Virginia, noticed mushrooms growing from under the baseboard in their classroom, their curiosity launched them and teacher Mary Breslin on an investigation into the causes and health effects of mold and fungus in school buildings.

After presenting their findings at a science fair, students wanted to keep working on policy solutions. That meant shifting their focus to learn in depth about how government works and, eventually, lobby their state legislature to pass a bill . Without a deep understanding of both science and social studies, and the ability to connect them, students could not have achieved the same results.

Their experience, one of many examples that education leader Ken Kay and I share in Redefining Student Success , shows what students can accomplish when they tackle real-world problems. It also underscores a challenge that teachers may face when projects don’t fit neatly into content silos.

Interdisciplinary learning can seem like a barrier for teachers who feel constrained by time, a prescribed curriculum, or a lack of opportunities to work with teachers from other disciplines. But the benefits can be profound , from increased engagement to academic gains. 

When students confront real-world problems, in school now or later in life, they may need more than one set of disciplinary lenses to see a complex issue or design a solution. Experts from Harvard Project Zero argue that addressing today’s most pressing issues—from environmental to social to economic—will require synthesizing knowledge from disparate sources.

A Continuum for Connecting

To help teachers think outside content silos when designing projects, I encourage them to start with problems that matter to students. A good prompt to encourage student brainstorming is “What’s a problem you care about that adults haven’t solved yet?” Thinking about the knowledge and skills essential to tackle a problem will lead to content goals, an important step in aligning projects to standards.

What if key learning goals are outside a teacher’s comfort zone or don’t fit into their planned curriculum? Instead of scaling back the project to fit a single content area, teachers can connect across disciplines with this continuum of connections: All-In , Just in Time , or the Handoff .

All-In: In this approach, significant learning goals for two or more content areas are incorporated in the same project. Elementary teachers can connect across content areas by bringing existing structures, such as literacy stations or math rotations, into project design in meaningful ways. ( This video from PBLWorks offers a good example.)

The All-In approach is routine in schools designed for teaming (as in these examples from High Tech High) or in interdisciplinary courses like Humanities or Environmental Economics.

Some teachers create opportunities to connect across content areas even without formal structures for teaming or dedicated time for planning with colleagues. Here’s an example of an engaging project with clear learning goals for English language arts and history that grew out of two teachers’ shared interest in podcasting. Regular check-ins, common deadlines, and shared strategies for assessment will keep the project on track.

Just in Time: Some projects focus primarily on one content area but bring in strategies from another discipline “just in time” for students to reach a solution or create a final product that would have been impossible otherwise. For instance, in a chemistry project, students designed and conducted lab experiments about water quality. To analyze their data for a journal article, they needed to apply statistical methods. That was when the teacher brought in guest statisticians as expert consultants.

In another example, students had a choice of final products to demonstrate historical thinking for a National History Day project. When one team proposed writing a one-act play, however, the teacher hesitated. He knew from formative assessments that the students’ research and interpretation of history was of high quality, but he had no experience in theater. Just in time, the school drama teacher agreed to step in as a consultant.

The Handoff: Less often, a project might start in one content area (or grade level) and then get handed off to another class for a new cycle of inquiry. For example, seventh-grade students at a K–12 school designed a community garden that reflected local culture and heritage, meeting learning goals in science and social studies. High school computer science students then took up the challenge of programming an irrigation system for the new garden. Students were able to see how different ways of thinking and problem-solving had improved the final product.

In another case, science students presented their research projects to an art class, inviting students to create visual interpretations of the findings. The artwork was displayed at a public exhibition alongside abstracts from the science projects.

All along the continuum, encouraging students to reflect on the disciplines that shaped their thinking helps to cement interdisciplinary learning.  

Support that Matters

To take advantage of opportunities for connecting across content areas, teachers need to know what’s happening outside their own classrooms. Leaders can support teachers by giving them time to meet outside their content areas for project brainstorming. A schoolwide project calendar is another useful tool to alert teachers to upcoming opportunities for collaboration.

Students aren’t the only ones who stand to benefit from interdisciplinary projects. Working with a teacher who has expertise in a content area different from yours “is a chance to be a learner again. That’s enlivening,” says Eitan Fire, a social studies teacher in Boulder, Colorado. His school encourages learning across disciplines , as in the History of Disease class he co-taught with a science teacher. “We both learned from each other.” 

Both Fire and Mary Breslin, the teacher whose students tackled classroom mold, have taken part in training from Earth Force , a nonprofit that supports teachers with tools and resources for environmental action civics. 

Having students more engaged in learning is another boost for teachers. “Students can burn out on lessons and worksheets about something like how a bill becomes a law,” Fire admits. “But if they’re learning in the context of civic engagement, focusing on local issues, it’s different.” 

His students recently began investigating the causes of pollution in a creek near their school. Fire invited a stormwater engineer to help students understand contributing factors, including the environmental impact of homeless encampments along the creek. That discussion led them to investigate causes of housing insecurity and income inequality as they considered sustainable solutions. As Fire acknowledges, these issues are complex and interconnected—but so is the learning. 

Caldwell Nanophotonic Materials and Devices Lab

Congrats to josh caldwell 2024 school of engineering interdisciplinary research award.

Posted by Natalie Baggett on Thursday, May 16, 2024 in News .

Josh Caldwell, Professor of Mechanical Engineering and Director of Vanderbilt’s Interdisciplinary Materials Science (IMS) graduate program, was honored with the Interdisciplinary Research Award for successfully leading interdisciplinary projects and integrating different fields of study to address complex research questions.

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UBCO researchers receive funding to incorporate human comfort into product design

May 16, 2024

Dr. Sepideh Pakpour, Assistant Professor of Mechanical Engineering at UBCO.

School of Engineering receives $1.65M grant for interdisciplinary projects engaging students and industry

Imagine a world where the clothing we wear is crafted with both human comfort and environmental sustainability in mind from the outset of the design process. Or a world where our health-care settings have been equipped and furnished to support all aspects of patients’ physical and psychological comfort.

Dr. Sepideh Pakpour, Assistant Professor in the School of Engineering at UBC Okanagan, along with her co-PIs (principal investigators) across the campus, have been awarded $1.65M toward their research in product design for human comfort to help make those goals a reality.

The funding, announced today , was made available through the NSERC Collaborative Research and Training Experience (CREATE) Program.

NSERC CREATE grants are designed to support training of highly qualified students and postdoctoral fellows from Canada and abroad through the development of innovative training programs that:

• encourage collaborative and integrative approaches, and address significant scientific challenges associated with Canada’s research priorities;
• facilitate the transition of new researchers from trainees to productive employees in the Canadian workforce.

At UBC Okanagan, the funding will allow Dr. Pakpour and a multi-disciplinary team of researchers to engage more than 80 students in research projects involving over 20 industry partners over the next six years.

“These projects will empower students, from undergraduate to their doctoral studies, to re-think the design process altogether. It’s about incorporating human comfort – which encapsulates all aspects of physical, physiological and psychological support – into product design from the very beginning of the process.”

Dr. Pakpour is working with fellow UBCO researchers in engineering, health sciences, computer science and psychology. Her industry partners span a diverse array of sectors, from clothing manufacturers to biomedical companies. Together, they can make leaps toward a future world with safer, comfort-enhanced products, says Dr. Pakpour.

“We are training and building talent for the future of this field, for industry and society. We are no longer living in an era where each discipline can, or should, work solo in the design process. These projects are examples of interdisciplinary teams, seamlessly working together to train the next generation of researchers and solve real-world problems facing diverse users.”

Under Dr. Pakpour’s leadership, the first cohort of students will join the research teams this September. They expect to engage at least 12 undergraduate and 68 graduate students over the next six years. “It’s very exciting we will be able to provide opportunities for this many students here in Canada in such an emerging and untraditional field. It will be very impactful, I am confident” said Dr. Pakpour. “We are grateful to NSERC for this funding which amplifies our research capacity not just within the School of Engineering but across disciplines on campus.”

Dr. Pakpour’s fundamental research interest and contributions fall into two categories: (a) better comprehending forces and factors forming Healthy Buildings and (b) human microbiome and human health. Under these categories, her research has continuously focused on translating basic microbiome discoveries into applications ranging from bioengineering and biomaterials. She has also co-led research on  using magnetic levitation to detect airborne viruses . Earlier this year, Dr. Pakpour was awarded a UBC Killam Faculty Research Fellowship Prize.

Posted in Faculty , News , Research | Tagged computer science , CREATE , engineering research , health , health care , Interdisciplinary , NSERC , psychology , school of engineering , Sepideh Pakpour , VPRI